Overview

Dataset statistics

Number of variables9
Number of observations500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.3 KiB
Average record size in memory72.3 B

Variable types

NUM8
BOOL1

Warnings

Serial No. has unique values Unique

Reproduction

Analysis started2020-09-28 14:22:11.512681
Analysis finished2020-09-28 14:22:25.165745
Duration13.65 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Serial No.
Real number (ℝ≥0)

UNIQUE

Distinct500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.5
Minimum1
Maximum500
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-28T10:22:25.254765image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile25.95
Q1125.75
median250.5
Q3375.25
95-th percentile475.05
Maximum500
Range499
Interquartile range (IQR)249.5

Descriptive statistics

Standard deviation144.4818328
Coefficient of variation (CV)0.5767737835
Kurtosis-1.2
Mean250.5
Median Absolute Deviation (MAD)125
Skewness0
Sum125250
Variance20875
MonotocityStrictly increasing
2020-09-28T10:22:25.400798image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
50010.2%
 
17110.2%
 
15810.2%
 
15910.2%
 
16010.2%
 
16110.2%
 
16210.2%
 
16310.2%
 
16410.2%
 
16510.2%
 
Other values (490)49098.0%
 
ValueCountFrequency (%) 
110.2%
 
210.2%
 
310.2%
 
410.2%
 
510.2%
 
ValueCountFrequency (%) 
50010.2%
 
49910.2%
 
49810.2%
 
49710.2%
 
49610.2%
 

GRE Score
Real number (ℝ≥0)

Distinct49
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean316.472
Minimum290
Maximum340
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-28T10:22:25.542829image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile298
Q1308
median317
Q3325
95-th percentile335
Maximum340
Range50
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.29514837
Coefficient of variation (CV)0.03569083007
Kurtosis-0.7110644626
Mean316.472
Median Absolute Deviation (MAD)8
Skewness-0.03984185809
Sum158236
Variance127.5803768
MonotocityNot monotonic
2020-09-28T10:22:25.688862image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%) 
312244.8%
 
324234.6%
 
316183.6%
 
321173.4%
 
322173.4%
 
327173.4%
 
314163.2%
 
311163.2%
 
320163.2%
 
317153.0%
 
Other values (39)32164.2%
 
ValueCountFrequency (%) 
29020.4%
 
29310.2%
 
29420.4%
 
29551.0%
 
29651.0%
 
ValueCountFrequency (%) 
34091.8%
 
33930.6%
 
33840.8%
 
33720.4%
 
33651.0%
 

TOEFL Score
Real number (ℝ≥0)

Distinct29
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107.192
Minimum92
Maximum120
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-28T10:22:25.819891image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum92
5-th percentile98
Q1103
median107
Q3112
95-th percentile118
Maximum120
Range28
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.08186766
Coefficient of variation (CV)0.05673807429
Kurtosis-0.6532454042
Mean107.192
Median Absolute Deviation (MAD)5
Skewness0.09560097236
Sum53596
Variance36.98911423
MonotocityNot monotonic
2020-09-28T10:22:25.926916image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%) 
110448.8%
 
105377.4%
 
104295.8%
 
112285.6%
 
107285.6%
 
106285.6%
 
103255.0%
 
102244.8%
 
100244.8%
 
99234.6%
 
Other values (19)21042.0%
 
ValueCountFrequency (%) 
9210.2%
 
9320.4%
 
9420.4%
 
9530.6%
 
9661.2%
 
ValueCountFrequency (%) 
12091.8%
 
119102.0%
 
118102.0%
 
11781.6%
 
116163.2%
 

University Rating
Real number (ℝ≥0)

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.114
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-28T10:22:26.039941image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.143511801
Coefficient of variation (CV)0.3672163779
Kurtosis-0.8100796635
Mean3.114
Median Absolute Deviation (MAD)1
Skewness0.09029498313
Sum1557
Variance1.307619238
MonotocityNot monotonic
2020-09-28T10:22:26.146973image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
316232.4%
 
212625.2%
 
410521.0%
 
57314.6%
 
1346.8%
 
ValueCountFrequency (%) 
1346.8%
 
212625.2%
 
316232.4%
 
410521.0%
 
57314.6%
 
ValueCountFrequency (%) 
57314.6%
 
410521.0%
 
316232.4%
 
212625.2%
 
1346.8%
 

SOP
Real number (ℝ≥0)

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.374
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-28T10:22:26.272993image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5
Q12.5
median3.5
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation0.9910036208
Coefficient of variation (CV)0.2937177299
Kurtosis-0.7057169536
Mean3.374
Median Absolute Deviation (MAD)0.5
Skewness-0.2289723963
Sum1687
Variance0.9820881764
MonotocityNot monotonic
2020-09-28T10:22:26.376017image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
48917.8%
 
3.58817.6%
 
38016.0%
 
2.56412.8%
 
4.56312.6%
 
2438.6%
 
5428.4%
 
1.5255.0%
 
161.2%
 
ValueCountFrequency (%) 
161.2%
 
1.5255.0%
 
2438.6%
 
2.56412.8%
 
38016.0%
 
ValueCountFrequency (%) 
5428.4%
 
4.56312.6%
 
48917.8%
 
3.58817.6%
 
38016.0%
 

LOR
Real number (ℝ≥0)

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.484
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-28T10:22:26.494044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3.5
Q34
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9254495739
Coefficient of variation (CV)0.2656284655
Kurtosis-0.7457485106
Mean3.484
Median Absolute Deviation (MAD)0.5
Skewness-0.1452903146
Sum1742
Variance0.8564569138
MonotocityNot monotonic
2020-09-28T10:22:26.599066image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
39919.8%
 
49418.8%
 
3.58617.2%
 
4.56312.6%
 
55010.0%
 
2.55010.0%
 
2469.2%
 
1.5112.2%
 
110.2%
 
ValueCountFrequency (%) 
110.2%
 
1.5112.2%
 
2469.2%
 
2.55010.0%
 
39919.8%
 
ValueCountFrequency (%) 
55010.0%
 
4.56312.6%
 
49418.8%
 
3.58617.2%
 
39919.8%
 

CGPA
Real number (ℝ≥0)

Distinct184
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.57644
Minimum6.8
Maximum9.92
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-28T10:22:26.739097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum6.8
5-th percentile7.638
Q18.1275
median8.56
Q39.04
95-th percentile9.6
Maximum9.92
Range3.12
Interquartile range (IQR)0.9125

Descriptive statistics

Standard deviation0.6048128003
Coefficient of variation (CV)0.07052026253
Kurtosis-0.5612783981
Mean8.57644
Median Absolute Deviation (MAD)0.46
Skewness-0.02661251732
Sum4288.22
Variance0.3657985234
MonotocityNot monotonic
2020-09-28T10:22:26.875128image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
891.8%
 
8.7691.8%
 
8.5471.4%
 
8.4571.4%
 
8.5671.4%
 
8.1271.4%
 
7.8861.2%
 
8.6461.2%
 
8.6661.2%
 
9.1161.2%
 
Other values (174)43086.0%
 
ValueCountFrequency (%) 
6.810.2%
 
7.210.2%
 
7.2110.2%
 
7.2310.2%
 
7.2510.2%
 
ValueCountFrequency (%) 
9.9210.2%
 
9.9110.2%
 
9.8720.4%
 
9.8610.2%
 
9.8210.2%
 

Research
Boolean

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
1
280 
0
220 
ValueCountFrequency (%) 
128056.0%
 
022044.0%
 
2020-09-28T10:22:27.103180image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Chance of Admit
Real number (ℝ≥0)

Distinct61
Distinct (%)12.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.72174
Minimum0.34
Maximum0.97
Zeros0
Zeros (%)0.0%
Memory size3.9 KiB
2020-09-28T10:22:27.203202image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.34
5-th percentile0.47
Q10.63
median0.72
Q30.82
95-th percentile0.94
Maximum0.97
Range0.63
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.141140404
Coefficient of variation (CV)0.1955557458
Kurtosis-0.4546817998
Mean0.72174
Median Absolute Deviation (MAD)0.1
Skewness-0.28996621
Sum360.87
Variance0.01992061363
MonotocityNot monotonic
2020-09-28T10:22:27.344234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.71234.6%
 
0.64193.8%
 
0.73183.6%
 
0.72163.2%
 
0.79163.2%
 
0.78153.0%
 
0.76142.8%
 
0.8132.6%
 
0.7132.6%
 
0.94132.6%
 
Other values (51)34068.0%
 
ValueCountFrequency (%) 
0.3420.4%
 
0.3620.4%
 
0.3710.2%
 
0.3820.4%
 
0.3910.2%
 
ValueCountFrequency (%) 
0.9740.8%
 
0.9681.6%
 
0.9551.0%
 
0.94132.6%
 
0.93122.4%
 

Interactions

2020-09-28T10:22:15.396552image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:15.567591image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:15.695620image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:15.823648image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:15.965680image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:16.105712image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:16.246743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:16.377773image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:16.511803image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:16.639831image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:16.767860image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:16.895889image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:17.038921image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:17.179952image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:17.320984image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:17.454014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:17.588044image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:17.716073image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:17.846102image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:17.975131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:18.117163image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:18.330211image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:18.471242image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:18.603272image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:18.737303image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:18.882334image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:19.028367image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:19.174400image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:19.333436image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:19.492471image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:19.650507image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:19.800541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:19.952575image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:20.096607image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:20.242640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:20.387672image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:20.544708image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:20.704744image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:20.862779image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:21.011812image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:21.163847image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:21.309879image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:21.453912image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:21.598944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:21.756979image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:21.913015image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:22.070050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:22.217083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:22.366117image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:22.499146image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:22.633176image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:22.766206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:23.006260image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:23.151293image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:23.297325image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:23.434356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:23.572387image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:23.707417image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:23.843448image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:23.978478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:24.127511image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:24.274545image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:24.421578image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:24.560609image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Correlations

2020-09-28T10:22:27.481264image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-28T10:22:27.693312image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-28T10:22:27.900359image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-28T10:22:28.111406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-09-28T10:22:24.797662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-28T10:22:25.049719image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
0133711844.54.59.6510.92
1232410744.04.58.8710.76
2331610433.03.58.0010.72
3432211033.52.58.6710.80
4531410322.03.08.2100.65
5633011554.53.09.3410.90
6732110933.04.08.2010.75
7830810123.04.07.9000.68
8930210212.01.58.0000.50
91032310833.53.08.6000.45

Last rows

Serial No.GRE ScoreTOEFL ScoreUniversity RatingSOPLORCGPAResearchChance of Admit
49049130710522.54.58.1210.67
4914922979943.03.57.8100.54
49249329810142.54.57.6910.53
4934943009523.01.58.2210.62
4944953019932.52.08.4510.68
49549633210854.54.09.0210.87
49649733711755.05.09.8710.96
49749833012054.55.09.5610.93
49849931210344.05.08.4300.73
49950032711344.54.59.0400.84